Object-centric representations
interpret scenes with “objects” as the basic building block (instead of individual pixels),
which is considered an essential step towards human-level generalization.
There is a rich literature on this topic,
for example, in [1]},
the IODINE model uses iterative variational inference to infer a set of latent variables recurrently,
with each representing one object in an image.
Similarly, MONet [2]} and GENESIS [3]} also adopt the multiple encode-decode steps.
In contrast, [4]} proposes Slot Attention,
which enables single step encoding-decoding with iterative attention.
However, all of the works mentioned above have only shown applications for synthetic datasets,  CLEVR [5]}.
In this paper, we are the first to demonstrate its use for object segmentation of realistic videos
by exploiting motion, where the challenging nuances in visual appearance
(the complex background textures) have been removed.
<FIGURE>